Going beyond digital libraries: a literature review of phygital user experience research methods
Bibliographic record
Abstract
Abstract This study investigates the emerging concept of “phygital” (physical and digital) user experience (UX) research within the context of public and academic libraries. It addresses two central questions: what considerations UX researchers and practitioners should keep in mind when studying phygital user interactions, and to what extent established UX research methods can be applied in these environments. Through a comprehensive literature review of English-language sources from the past decade across library and information science (LIS), human–computer interaction (HCI), and marketing, the authors examine the applicability of established UX research methods to phygital contexts. The study highlights several key considerations for library UX professionals, including the need to adapt methodologies, incorporate accessibility and inclusion frameworks, and navigate organizational challenges. The findings suggest that while existing UX literature offers valuable guidance, interdisciplinary collaboration drawing from fields such as marketing, HCI, and design justice can further support libraries in developing innovative and inclusive phygital user experiences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.029 | 0.028 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".